diff --git a/examples/heisenberg_tDMRG/heisenberg_groundstate.ipynb b/examples/heisenberg_tDMRG/heisenberg_groundstate.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3b21d9a12d3095f9fe09f6fd404feba112dc2a1d
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+++ b/examples/heisenberg_tDMRG/heisenberg_groundstate.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import transpose as tr, conjugate as co\n",
+    "from scipy.linalg import expm, svd\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = 16, 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some useful functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def dot(A,B):\n",
+    "    \"\"\" Does the dot product like np.dot, but preserves the shapes also for singleton dimenstions \"\"\"\n",
+    "    s1 = A.shape\n",
+    "    s2 = B.shape\n",
+    "    return np.dot(A,B).reshape((s1[0],s2[1]))\n",
+    "\n",
+    "def truncated_svd(thetas,chi):\n",
+    "    \"\"\" Does an svd on two-site matrix thetas, and truncates to chi or the last nonzero singular value \"\"\"\n",
+    "    U, S, Vh = svd(thetas,full_matrices = False)\n",
+    "    # trunkieren\n",
+    "    ind = np.where(np.isclose(np.cumsum(S[::-1])[::-1],0))[0]\n",
+    "    if len(ind)>0:\n",
+    "        chi_tr = min(chi,max(1,ind[0]))\n",
+    "    else:\n",
+    "        chi_tr = chi\n",
+    "    S=S[:chi_tr]\n",
+    "    w=1.-np.sum(S**2)\n",
+    "    S/=math.sqrt(1-w)\n",
+    "    U=U[:,:chi_tr]\n",
+    "    Vh=Vh[:chi_tr,:]\n",
+    "    return U, S, Vh\n",
+    "\n",
+    "def left_canonize(thetas,s,chi, return_S = False):\n",
+    "    \"\"\" Splits up a two-site matrix thetas into two one-site matrices such that the left one is canonized \"\"\"\n",
+    "    da, dg = thetas.shape[2], thetas.shape[3]\n",
+    "    thetas = thetas.transpose((2,0,3,1)).reshape((da*s,dg*s)) # combine indizes\n",
+    "    U, S, Vh = truncated_svd(thetas,chi)\n",
+    "\n",
+    "    db     = len(S)\n",
+    "    U      = U.reshape((da,s,db)).transpose((1,0,2))\n",
+    "    Vh     = Vh.reshape((db,dg,s)).transpose((2,0,1))    \n",
+    "    for s2 in range(s):\n",
+    "        Vh[s2] = dot(np.diag(S),Vh[s2])\n",
+    "    if return_S:\n",
+    "        return U, Vh, S\n",
+    "    else:\n",
+    "        return U, Vh\n",
+    "\n",
+    "def right_canonize(thetas,s,chi, return_S = False):\n",
+    "    \"\"\" Splits up a two-site matrix thetas into two one-site matrices such that the right one is canonized \"\"\"\n",
+    "    da, dg = thetas.shape[2], thetas.shape[3]\n",
+    "    thetas = thetas.transpose((2,0,3,1)).reshape((da*s,dg*s)) # combine indizes\n",
+    "    U, S, Vh = truncated_svd(thetas,chi)\n",
+    "\n",
+    "    db     = len(S)\n",
+    "    U      = U.reshape((da,s,db)).transpose((1,0,2))\n",
+    "    Vh     = Vh.reshape((db,dg,s)).transpose((2,0,1))    \n",
+    "    for s1 in range(s):\n",
+    "        U[s1] = dot(U[s1],np.diag(S))\n",
+    "    if return_S:\n",
+    "        return U, Vh, S\n",
+    "    else:\n",
+    "        return U, Vh\n",
+    "\n",
+    "\n",
+    "def apply_two_site_H(theta,H,s):\n",
+    "    \"\"\" Applies a two-site operator on the two-site matrix theta \"\"\"\n",
+    "    thetas = np.zeros_like(theta)\n",
+    "    for s1p in range(s):\n",
+    "        for s2p in range(s):\n",
+    "            for s1 in range(s):\n",
+    "                for s2 in range(s):\n",
+    "                    thetas[s1p,s2p] += H[s1p*s+s2p,s1*s+s2] * theta[s1,s2]\n",
+    "\n",
+    "    return thetas\n",
+    "\n",
+    "def combine_two_matrices(M1,M2,s):\n",
+    "    \"\"\" Combines the two neighbouring one-site matrices M1 and M2 into a two-site matrix theta \"\"\"\n",
+    "    da, db, dg=M1.shape[1], M1.shape[2], M2.shape[2]\n",
+    "    assert M1.shape[2] == M2.shape[1]\n",
+    "\n",
+    "    theta = np.zeros((s,s,da,dg),dtype=complex)\n",
+    "    for s1 in range(s):\n",
+    "        for s2 in range(s):\n",
+    "            theta[s1,s2]=dot(M1[s1],M2[s2])\n",
+    "\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Definition of system and initializiation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "J   = 1.\n",
+    "L   = 20     # Length of chain\n",
+    "s   = 2      # Local dimension of Hilbert space\n",
+    "dt  = 0.05   # The imaginary timestep (should be 0.01 or smaller for good accuracy)\n",
+    "chi = 60     # The maximum matrix dimension, from which on the matrices are truncated\n",
+    "nmax = 10000 # Maximum number of iterations\n",
+    "nskip = 10   # Check energy convergence after nskip steps\n",
+    "\n",
+    "# Two-site Hamiltonian\n",
+    "H  = np.array([[J/4,0,0,0],\n",
+    "              [0,-J/4,J/2,0],\n",
+    "              [0,J/2,-J/4,0],\n",
+    "              [0,0,0,J/4]])\n",
+    "\n",
+    "# Imaginary time evolution operator\n",
+    "exp_beta_H=expm(-H*dt);\n",
+    "\n",
+    "# antiferromagnetic starting configuration\n",
+    "M = []\n",
+    "for i in range(L):\n",
+    "    ar = np.zeros((2,1,1),dtype=complex)\n",
+    "    ar[0,0,0] = i%2\n",
+    "    ar[1,0,0] = (i+1)%2\n",
+    "    M.append(ar)\n",
+    "\n",
+    "even = np.array(range(0,L-1,2))\n",
+    "odd  = np.array(range(L+L%2-2,0,-2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imaginary time evolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 dE =  5.2022859299\n",
+      "10 dE =  2.41237932615\n",
+      "20 dE =  0.616048162871\n",
+      "30 dE =  0.19945192398\n",
+      "40 dE =  0.0856110936378\n",
+      "50 dE =  0.0446638884127\n",
+      "60 dE =  0.0266683370958\n",
+      "70 dE =  0.0176509913134\n",
+      "80 dE =  0.0127047223848\n",
+      "90 dE =  0.00977408061919\n",
+      "100 dE =  0.00789488605981\n",
+      "110 dE =  0.00658572945765\n",
+      "120 dE =  0.00559930606822\n",
+      "130 dE =  0.00480731763688\n",
+      "140 dE =  0.00414298945949\n",
+      "150 dE =  0.0035712463933\n",
+      "160 dE =  0.00307305023898\n",
+      "170 dE =  0.00263722905494\n",
+      "180 dE =  0.00225631290084\n",
+      "190 dE =  0.00192449985237\n",
+      "200 dE =  0.00163673118822\n",
+      "210 dE =  0.00138832203542\n",
+      "220 dE =  0.0011748521653\n",
+      "230 dE =  0.000992164333084\n",
+      "240 dE =  0.000836395474847\n",
+      "250 dE =  0.000704007231025\n",
+      "260 dE =  0.000591803752473\n",
+      "270 dE =  0.000496934572579\n",
+      "280 dE =  0.000416884722746\n",
+      "290 dE =  0.000349455574773\n",
+      "300 dE =  0.000292739848431\n",
+      "310 dE =  0.000245093656\n",
+      "320 dE =  0.000205107728403\n",
+      "330 dE =  0.000171579325436\n",
+      "340 dE =  0.000143485782885\n",
+      "350 dE =  0.000119960236445\n",
+      "360 dE =  0.000100269773123\n",
+      "370 dE =  8.37960556659e-05\n",
+      "380 dE =  7.00183361975e-05\n",
+      "390 dE =  5.84987014349e-05\n",
+      "400 dE =  4.88693502501e-05\n",
+      "410 dE =  4.08216810968e-05\n",
+      "420 dE =  3.40969704631e-05\n",
+      "430 dE =  2.84784768727e-05\n",
+      "440 dE =  2.37847443216e-05\n",
+      "450 dE =  1.98639157318e-05\n",
+      "460 dE =  1.65889561448e-05\n",
+      "470 dE =  1.38536333161e-05\n",
+      "480 dE =  1.15691357934e-05\n",
+      "490 dE =  9.661232097e-06\n",
+      "500 dE =  8.06788848529e-06\n",
+      "510 dE =  6.73727353018e-06\n",
+      "520 dE =  5.62608618715e-06\n",
+      "530 dE =  4.69815473814e-06\n",
+      "540 dE =  3.92326507992e-06\n",
+      "550 dE =  3.27618108287e-06\n",
+      "560 dE =  2.73582595511e-06\n",
+      "570 dE =  2.28459734686e-06\n",
+      "580 dE =  1.90779520892e-06\n",
+      "590 dE =  1.59314354775e-06\n",
+      "600 dE =  1.33039094941e-06\n",
+      "610 dE =  1.11097674527e-06\n",
+      "620 dE =  9.27752324742e-07\n",
+      "630 dE =  7.747481785e-07\n",
+      "640 dE =  6.46979531282e-07\n",
+      "650 dE =  5.40283849659e-07\n",
+      "660 dE =  4.51185265149e-07\n",
+      "670 dE =  3.76781240874e-07\n",
+      "680 dE =  3.1464805339e-07\n",
+      "690 dE =  2.62761810532e-07\n",
+      "700 dE =  2.19432413573e-07\n",
+      "710 dE =  1.83248596741e-07\n",
+      "720 dE =  1.53031841421e-07\n",
+      "730 dE =  1.27798031713e-07\n",
+      "740 dE =  1.06725389415e-07\n",
+      "750 dE =  8.91276528137e-08\n",
+      "760 dE =  7.44317638635e-08\n",
+      "770 dE =  6.21591595973e-08\n",
+      "780 dE =  5.19102449914e-08\n",
+      "790 dE =  4.33512816755e-08\n",
+      "800 dE =  3.62035965651e-08\n",
+      "810 dE =  3.02344727032e-08\n",
+      "820 dE =  2.52495695463e-08\n",
+      "830 dE =  2.10865902517e-08\n",
+      "840 dE =  1.76100023452e-08\n",
+      "850 dE =  1.47066288037e-08\n",
+      "860 dE =  1.22819674431e-08\n",
+      "870 dE =  1.02570609783e-08\n",
+      "880 dE =  8.56601900523e-09\n",
+      "Converged after 880 steps!\n",
+      "Ground-state energy: -8.68010558621\n"
+     ]
+    }
+   ],
+   "source": [
+    "enold = 0. # for checking energy convergence\n",
+    "\n",
+    "for n in range(nmax):\n",
+    "    # ++++ Trotter scheme: first even bonds, than odd bonds\n",
+    "    # === apply exp_beta_H on all even bonds ===\n",
+    "    for j in even:\n",
+    "        # go from left to right\n",
+    "        theta = combine_two_matrices(M[j],M[j+1],s)\n",
+    "        thetas = apply_two_site_H(theta,exp_beta_H,s)        \n",
+    "        M[j], M[j+1]   = left_canonize(thetas,s,chi)\n",
+    "\n",
+    "        # advance left canonization by a further step\n",
+    "        if j < L-2:\n",
+    "            theta = combine_two_matrices(M[j+1],M[j+2],s)            \n",
+    "            M[j+1], M[j+2]  = left_canonize(theta,s,chi)\n",
+    "    # === apply exp_beta_H on all even bonds ===\n",
+    "\n",
+    "    # === renormalize the state on the last site ===\n",
+    "    da = M[L-1].shape[1]\n",
+    "    theta = M[L-1].reshape((s*da,1))\n",
+    "    U, _, _ = truncated_svd(theta,chi) # throw away norm\n",
+    "    M[L-1] = U.reshape((s,da,1))\n",
+    "    # === renormalize the state on the last site ===\n",
+    "\n",
+    "    if L%2 == 0:\n",
+    "        # the right-most matrix has to be right canonized in this case\n",
+    "        theta = combine_two_matrices(M[L-2],M[L-1],s)\n",
+    "        M[L-2], M[L-1]  = right_canonize(theta,s,chi)\n",
+    "\n",
+    "    # === apply exp_beta_H on all odd bonds ===\n",
+    "    for j in odd:\n",
+    "        # go from right to left\n",
+    "        theta  = combine_two_matrices(M[j-1],M[j],s)\n",
+    "        thetas = apply_two_site_H(theta,exp_beta_H,s)\n",
+    "        M[j-1], M[j]   = right_canonize(thetas,s,chi)\n",
+    "\n",
+    "        # advance right canonization by a further step\n",
+    "        if j>1:\n",
+    "            theta = combine_two_matrices(M[j-2],M[j-1],s)\n",
+    "            M[j-2], M[j-1]  = right_canonize(theta,s,chi)\n",
+    "    # === apply exp_beta_H on all odd bonds ===\n",
+    "\n",
+    "    # === renormalize the state on the first site ===\n",
+    "    db = M[0].shape[2]\n",
+    "    theta = M[0].reshape((1,s*db))\n",
+    "    _, _, Vh = truncated_svd(theta,chi) # throw away norm\n",
+    "    M[0] = Vh.reshape((s,1,db))\n",
+    "    # === renormalize the state on the first site ===\n",
+    "\n",
+    "    # ++++ Calculate energy and check convergence ++++\n",
+    "    if n%nskip == 0:\n",
+    "        # Measure Energy via sum of two-site Operators H\n",
+    "        en = 0.\n",
+    "        for j in range(L-1):            \n",
+    "            theta  = combine_two_matrices(M[j],M[j+1],s)\n",
+    "            thetas = apply_two_site_H(theta,H,s)            \n",
+    "            for s1 in range(s):\n",
+    "                for s2 in range(s):\n",
+    "                    en += np.trace(dot(thetas[s1,s2],co(tr(theta[s1,s2])))).real\n",
+    "            M[j], M[j+1]   = left_canonize(theta,s,chi) # Proceed with left canonization\n",
+    "\n",
+    "        # make right canonized MPS for next tDMRG step\n",
+    "        for j in range(L-1,0,-1):\n",
+    "            theta = combine_two_matrices(M[j-1],M[j],s)\n",
+    "            M[j-1], M[j]  = right_canonize(theta,s,chi)\n",
+    "\n",
+    "        print(n,'dE = ',abs(en-enold))\n",
+    "        if abs(en-enold) < 1e-8:\n",
+    "            print(\"Converged after \"+str(n)+\" steps!\")\n",
+    "            print(\"Ground-state energy: \" + str(en))\n",
+    "            break\n",
+    "        enold = en\n",
+    "    # === check convergence ===\n",
+    "\n",
+    "if n == nmax:\n",
+    "    print(\"Maximum iterations reached! Convergence criterium not satisfied!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Measure and plot the Entanglement Entropy between all sites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AEyWPCTBRiJhIgIcPl5XNffvMxBRWtvt/HQUFkhAePWo7EvcFpfzZMX++bElz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\ngQAMAAAAACiF/x91AUNG9p2rGAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c5ae4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ent = np.zeros((L-1))\n",
+    "## warning measurment assumes right canonized MPS\n",
+    "for j in range(L-1):\n",
+    "    theta  = combine_two_matrices(M[j],M[j+1],s)\n",
+    "    M[j], M[j+1], S = left_canonize(theta,s,chi, return_S = True)\n",
+    "    ent[j] = -np.sum(S**2*np.log(S**2))\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(ent,'-o')\n",
+    "plt.xlabel('site i')\n",
+    "plt.ylabel(r'$S(\\rho_i)$')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}